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M-SC in Computer Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science at SRM Institute of Science and Technology Chengalpattu?
This M.Sc. Computer Science program at SRM Institute of Science and Technology focuses on equipping students with advanced theoretical knowledge and practical skills in cutting-edge areas of computing. It''''s designed to meet the growing demand for skilled professionals in India''''s booming IT sector, emphasizing both foundational principles and emerging technologies like AI, Machine Learning, and Cloud Computing.
Who Should Apply?
This program is ideal for Bachelor''''s degree holders in Computer Science or related fields seeking to deepen their expertise and advance their careers. It caters to fresh graduates aiming for specialized roles in software development, data science, or cybersecurity, as well as working professionals looking to upskill and remain competitive in India''''s rapidly evolving tech landscape.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths in India as Data Scientists, Machine Learning Engineers, Cloud Architects, Cybersecurity Analysts, and Software Developers. Entry-level salaries typically range from INR 5-8 LPA, with experienced professionals earning upwards of INR 15-20 LPA. The curriculum aligns with professional certifications, fostering strong growth trajectories in leading Indian and multinational companies.
Student Success Practices
Foundation Stage
Strengthen Core Programming & Data Structures- (Semester 1-2)
Dedicate consistent time to coding practice. Solve problems on competitive programming platforms and understand time and space complexity thoroughly. Focus on languages like C++, Java, or Python.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, CodeChef, NPTEL courses on Algorithms
Career Connection
Mastering these fundamentals is crucial for cracking technical interviews, building efficient software, and pursuing roles in development, data science, and competitive programming.
Active Participation in Lab Sessions- (Semester 1-2)
Maximize learning from practical applications of theoretical concepts. Don''''t just complete assignments; experiment with variations, understand error messages, and collaborate with peers to debug complex code.
Tools & Resources
IDEs like VS Code, Eclipse, Jupyter Notebooks, Online documentation, Peer study groups
Career Connection
Develops hands-on problem-solving skills, crucial for real-world projects and immediate contribution to a development or research team, improving employability.
Build a Strong Mathematical Foundation- (Semester 1-2)
Understand the statistical and mathematical underpinnings of computer science. Pay close attention to courses like Applied Statistics, as these concepts are vital for Machine Learning, Data Science, and Algorithm analysis.
Tools & Resources
Khan Academy, Coursera courses on Linear Algebra and Probability, Recommended textbooks, Academic tutoring support
Career Connection
Forms the bedrock for advanced topics in AI/ML, data analytics, and research, opening doors to specialized analytical positions and academic pursuits.
Intermediate Stage
Advanced Stage
Deep Dive into Electives & Specialization- (Semester 3-4)
Choose electives strategically based on your career goals and build a specialized portfolio. Beyond coursework, pursue online certifications in your chosen area (e.g., Deep Learning, Cybersecurity).
Tools & Resources
Kaggle, GitHub, Coursera/Udemy specialized courses, Relevant industry blogs and whitepapers
Career Connection
Develops a unique and in-demand skill set, making you stand out to recruiters and helping secure specialized roles in your preferred domain within the Indian tech industry.
Actively Engage in Research & Projects- (Semester 3-4)
Leverage the Mini Project and Project Work to solve real-world problems. Identify a unique problem, conduct thorough literature review, propose innovative solutions, implement, and present your findings effectively. Seek faculty mentorship.
Tools & Resources
Research papers (IEEE, ACM), Academic databases (Scopus, Web of Science), Project management tools, Collaboration platforms like Google Docs
Career Connection
Essential for showcasing problem-solving abilities, research aptitude, and contributes significantly to your resume and potential for higher studies or R&D roles in India.
Network and Prepare for Placements- (undefined)
Build professional connections and hone interview skills. Attend webinars, workshops, and career fairs. Connect with alumni and industry professionals on LinkedIn. Practice aptitude tests, technical interviews, and soft skills rigorously.
Tools & Resources
LinkedIn, Glassdoor, Interview preparation books and online platforms, Mock interview sessions, SRMIST''''s placement cell
Career Connection
Directly impacts securing internships and full-time placements in leading companies across India, ensuring a smooth transition from academia to a successful professional career.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s degree in Computer Science/Computer Technology/IT/Computer Applications/Software Engineering or equivalent degree with minimum 50% aggregate.
Duration: 2 years (4 semesters)
Credits: 90 Credits
Assessment: Internal: Theory: 40%, Practical: 50%, External: Theory: 60%, Practical: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PMA23301 | Applied Statistics for Computer Science | Core | 4 | Introduction to Statistics, Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Correlation and Regression |
| PCS23301 | Advanced Data Structures and Algorithms | Core | 4 | Algorithm Analysis, Advanced Data Structures (Trees, Graphs, Heaps), Sorting and Searching, Graph Algorithms, Dynamic Programming |
| PCS23302 | Object Oriented Software Engineering | Core | 4 | Software Engineering Principles, Object-Oriented Concepts, UML Modeling, Software Design Patterns, Software Testing |
| PCS23303 | Advanced Operating Systems | Core | 4 | Operating System Concepts, Process Management, Memory Management, File Systems, Distributed Operating Systems |
| PCS23304 | Advanced Data Structures and Algorithms Lab | Practical | 2 | Implementation of data structures, Graph algorithms, Dynamic programming problems, Algorithmic efficiency analysis |
| PCS23305 | Object Oriented Software Engineering Lab | Practical | 2 | UML Diagramming tools, Object-oriented design implementation, Software testing techniques |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCS23306 | Advanced Database Management Systems | Core | 4 | Relational Database Design, SQL Query Optimization, Transaction Management, Concurrency Control, Distributed Databases, NoSQL Databases |
| PCS23307 | Machine Learning | Core | 4 | Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Deep Learning Fundamentals, Model Evaluation |
| PCS23308 | Cryptography and Network Security | Core | 4 | Network Security Principles, Cryptographic Algorithms, Public Key Infrastructure, Network Attacks, Security Protocols |
| PCS23309 | Cloud Computing | Core | 4 | Cloud Computing Architecture, Cloud Service Models (IaaS, PaaS, SaaS), Virtualization, Cloud Security, Cloud Deployment Models |
| PCS23310 | Advanced Database Management Systems Lab | Practical | 2 | SQL queries, Database design, Transaction control, NoSQL database operations |
| PCS23311 | Machine Learning Lab | Practical | 2 | Supervised learning algorithms implementation, Unsupervised learning implementation, Model training and evaluation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCS23312 | Research Methodology and IPR | Core | 3 | Research Design, Data Collection Methods, Statistical Analysis, Report Writing, Intellectual Property Rights |
| PCS23313 | Compiler Design | Core | 4 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| PCS23E01 | Image Processing | Elective (Professional Elective – I) | 3 | Image Fundamentals, Image Enhancement, Image Restoration, Image Segmentation, Feature Extraction |
| PCS23E02 | Internet of Things | Elective (Professional Elective – I) | 3 | IoT Architecture, IoT Protocols, IoT Devices, Sensor Networks, Data Analytics in IoT |
| PCS23E03 | Artificial Intelligence | Elective (Professional Elective – I) | 3 | AI Agents, Search Algorithms, Knowledge Representation, Machine Learning Basics, Expert Systems |
| PCS23E04 | Big Data Analytics | Elective (Professional Elective – I) | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce, Spark, Data Visualization, Big Data Security |
| PCS23E05 | Data Warehousing and Mining | Elective (Professional Elective – II) | 3 | Data Warehousing Concepts, OLAP, Data Preprocessing, Data Mining Techniques, Association Rule Mining, Classification |
| PCS23E06 | Natural Language Processing | Elective (Professional Elective – II) | 3 | NLP Fundamentals, Text Preprocessing, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation |
| PCS23E07 | Block Chain Technology | Elective (Professional Elective – II) | 3 | Cryptographic Principles, Blockchain Architecture, Consensus Mechanisms, Smart Contracts, DApps |
| PCS23E08 | Web Programming | Elective (Professional Elective – II) | 3 | HTML, CSS, JavaScript, Server-Side Scripting, Web Frameworks, Database Connectivity |
| PCS23314 | Compiler Design Lab | Practical | 2 | Lexical analyzer implementation, Parser implementation, Code generation for simple constructs |
| PCS23PL1 | Mini Project | Project | 2 | Problem identification, Design and implementation, Project reporting |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PCS23E09 | Cyber Security | Elective (Professional Elective – III) | 3 | Network Security Threats, Digital Forensics, Ethical Hacking, Security Policies, Incident Response |
| PCS23E10 | Augmented Reality and Virtual Reality | Elective (Professional Elective – III) | 3 | AR/VR Devices, 3D Graphics, Interaction Techniques, Tracking, Applications of AR/VR |
| PCS23E11 | Software Testing and Quality Assurance | Elective (Professional Elective – III) | 3 | Software Testing Principles, Test Plan, Test Case Design, Quality Assurance Standards, Automated Testing |
| PCS23E12 | Green Computing | Elective (Professional Elective – III) | 3 | Energy Efficiency in IT, Sustainable Computing, Green IT Initiatives, E-waste Management, Carbon Footprint |
| PCS23E13 | Deep Learning | Elective (Professional Elective – IV) | 3 | Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Deep Learning Architectures, Transfer Learning |
| PCS23E14 | Bio Informatics | Elective (Professional Elective – IV) | 3 | Biological Databases, Sequence Alignment, Phylogenetic Trees, Protein Structure Prediction, Genomics |
| PCS23E15 | Robotic Process Automation | Elective (Professional Elective – IV) | 3 | RPA Fundamentals, Process Analysis, Bot Development, RPA Tools, Enterprise RPA Implementation |
| PCS23E16 | Quantum Computing | Elective (Professional Elective – IV) | 3 | Quantum Mechanics Basics, Qubits, Quantum Gates, Quantum Algorithms, Quantum Cryptography |
| PCS23PW1 | Project Work | Project | 10 | Literature Survey, Problem Definition, System Design, Implementation and Testing, Thesis Writing |
